Spatio-Temporal Pattern Mining
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Spatio-Temporal Pattern Mining for Multi-Jurisdiction Multi-Timeframe (MJMT) Activity Datasets Investigators: Shashi Shekhar,(U Minnesota) Bhavani T., L. Khan(U.T.Dallas) Start Date: Summer 2007. Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

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Motivation: Many Applications Example: Urban Crime patterns, Sensor Data, …

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Motivation many applications example urban crime patterns sensor data

Spatio-Temporal Pattern Miningfor Multi-Jurisdiction Multi-Timeframe (MJMT) Activity DatasetsInvestigators: Shashi Shekhar,(U Minnesota) Bhavani T., L. Khan(U.T.Dallas) Start Date: Summer 2007

  • Motivation: Many Applications

    • Example: Urban Crime patterns, Sensor Data, …

    • Pattern Families: Hotspots, Journey to crime, trends, …

    • Tasks: Crime Prevention, Patrol routes/schedule, …

  • Problem Definition

    • Inputs: (i) Activity reports with location and time

      • Pattern families

    • Output: Pattern instances

    • Objective Function: Accuracy, Scalability

    • Constraints:Urban transportation network


Challenge 1 spatio temporal st nature of patterns

Challenge 1: Spatio-Temporal (ST) Nature of Patterns

  • State of the Art: Environmental Criminology

    • Spatial Methods: Hotspots, Spatial Regression

    • Space-time interaction (Knox test)

  • Critical Barriers: richer ST semantics

    • Ex. Trends, periodicity, displacement

  • Approach:

    • Categorize pattern families

    • Quantify: interest measures

    • Design scalable algorithms

    • Evaluate with crime datasets

  • Challenges: Trade-off b/w

    • Semantic richness and

    • Scalable algorithms

High activity: 2300 -0000 hrs

Rings = weekdays; Slices = hour

(Source: US Army ERDC, TEC)


2 activites on urban infrastructure st networks

2: Activites on Urban Infrastructure ST Networks

  • State of the Art: Environmental Criminology

    • Largely geometric Methods

    • Few Network Methods: Journey to Crime (J2C)

  • Critical Barriers:

    • Scale: Houston – 100,000 crimes / year

    • Network based explanation

    • Spatio-temporal networks

  • Approaches:

    • Scalable algorithms for J2C analysis

    • Network based explanatory models

    • Time-aggregated graphs (TAG)

  • Challenges: Key assumptions violated!

    • Ex. Prefix optimality of shortest paths

    • Can’t use Dijktra’s, A*, etc.

(a) Input: Pink lines connect crime location & criminal’s residence

(b) Output: Journey- to-Crime

(thickness = route popularity)

Source: Crimestat


3 multi jurisdiction multi temporal mjmt data

N2

N3

N4

N5

N1

R3

R1

R2

Transition Edge

Road Intersections

Subway Stations

3: Multi-Jurisdiction Multi-Temporal (MJMT) Data

  • State of the Art:

    • Spatial, ST ontologies

    • Few network ontologies

  • Critical Barriers:

    • Heterogeneity across networks

    • Uncertainty – map accuracy, gps, …

  • Approach:

    • Ontologies: ST Network activities

    • Integration methods: MJMT data

    • Location accuracy models

  • Challenges:

    • Test datasets

    • Evaluation methods


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